Our framework is a general one that can be used together with any differentiable search method. Specifically, we apply our framework to the following NAS methods: 1) DARTS [1]}, 2) P-DARTS [2]}, 3) DARTS+ [3]}, 4) DARTS\(^{-}\)  [4]}. The search space in these methods are similar. The candidate operations include: \(3\times 3\)  and \(5\times 5\)  separable convolutions, \(3\times 3\)  and \(5\times 5\)  dilated separable convolutions, \(3\times 3\)  max pooling, \(3\times 3\)  average pooling, identity, and zero. In LPT, the network of the learner is a stack of multiple cells, each consisting of 7 nodes. For the data encoder of the tester, we tried ResNet-18 and ResNet-50 [5]}. For the test creator and target-task executor, they are set to one feed-forward layer. \(\lambda \)  and \(\gamma \)  are both set to 1.
